BeMap

BeMap: Balanced Message Passing for Fair Graph Neural Network (LOG 2023)

Links: Paper | Poster | arXiv

BeMap is a fair message passing method for solving the unfairness problem on graph machine learning. It is a model-agnostic easy-to-implement solution that is able to effectively mitigate structural bias for most typical GNNs, such as GCN and GAT.

Cite Us

If you find this repository helpful in your work or research, we would greatly appreciate citations to the following paper:

@inproceedings{lin2023bemap,
  title={BeMap: Balanced Message Passing for Fair Graph Neural Network},
  author={Lin, Xiao and Kang, Jian and Cong, Weilin and Tong, Hanghang},
  booktitle={The Second Learning on Graphs Conference},
  year={2023}
}

Background

We introduce a novel fair message passing method named BeMap. It creates a fair graph structure for each epoch by leveraging a balance-aware sampling strategy to balance the number of the 1-hop neighbors of each node among different demographic groups. The intuition of mitigating structural bias is to push the bias residual for all the nodes to converge at the same fair centroid. The figure below gives an overview of BeMap.

Requirement

Main dependencies:

• Python (= 3.9)
• Pytorch (= 2.1.0)
• dgl (= 2.1.0)
• Pandas (= 2.2.1)
• Numpy (= 1.26.4)
• scikit-learn (= 1.4.1)

To install requirements, run:

pip install -r requirements.txt

Usage

1. Datasets

• Pokec_z is stored in dataset/pokec as region_job.xxx, introduced by Enyan Dai et al.
• NBA is stored in dataset/NBA as nba.xxx, introduced by Enyan Dai et al..
• Recidivism is stored in dataset/bail, introduced by Chirag Agarwa et al..
• Credit is stored in dataset/credit_compressed, introduced by Chirag Agarwa et al..
  • We can extract credit.zip using the following command:

    unzip -d ./dataset ./dataset/credit_compressed/credit.zip

2. Running BeMap

We can run BeMap by the following command:

python train_bemap.py --dataset dataset_name --model model_name

For example, if we want to run BeMap on NBA dataset with GCN, we can excute the following command:

python train_bemap.py --dataset nba --model gcn

3. Experiment results

an example when running BeMap on the Recidivism dataset with GCN:

Overall results on real-world datasets

Ablation study on balance-aware sampling strategy

Contact

If you have any questions or want to use the code, feel free to contact:

• Xiao Lin (xiaol13@illinois.edu)